What Translators should know in the AI Age

People don’t want lawyers – they want justice! In the same way, businesses don’t want translators, they want their content out there in other languages. How can we best engage with these content providers - and the translators that work for them?

Most of the discussion about the future role of translators and the challenges they face has been carried out within the confines of the enterprise translation industry. Whose basic aim is to deliver translations as a best business fit given the available resources, human or otherwise.

Let’s call this Big Translation, a business worth some $40 billion globally. I say “confines” because beyond the office door, there’s a vast geography of translation activity that covers literature, some audiovisual, everyday spoken interpretation in hospitals, law courts or the auditoria of large organizations, as well as school-room exercises or nonce help for tourists. Let’s call that multifarious activity Craft Translation. No one knows how much this market is worth.

TAUS wants to deepen and enrich the conversation around the Big/Craft divide: between people and software, general automation and personal autonomy. To kickstart this, we plan to launch a series of training modules to help translators deepen their understanding of 21st century translation automation.

Here are three key points we all need to engage with.

Understanding what AI is and isn’t:

Machine learning/AI uses software to generate predictions based on pattern-finding – not truths about the world. In the case of neural MT (NMT), the software learns enough to predict that a given source sequence of words, starting from characters (not meanings!), should deliver that parallel target sequence. This is what learning is: using strong hypotheses to guess what seems to work. In other words, the machine is there to help us make better decisions. But only we can decide.

Why NMT is important today:

For the immediate future, NMT will provide a powerful solution for accelerating the production of useful translation sequences, provided that a number of technical conditions are met. For example, NMT systems today depend on powerful hardware, which comes at a price. At the same time, they are largely supported by a network of highly active research groups who are steadily expanding the range, depth, and technical underpinning of the technology. The debate is no longer about a bigger “cloud” or more data-sharing. Tomorrow’s neural will almost certainly be far more sophisticated than today’s. So we need to maintain an intelligent dialog with that R&D community.

Why LSPs and translators should be concerned:

This investment in neural MT in a general AI context is set to continue. It will almost certainly stimulate new competitors in the industry as suppliers design and adapt their own systems to specific needs. This in turn will mean mapping new skills in trans-reviewing/creating/lating to a new generation of translation specialists. Now is the time for the industry (LSPs and their staff/suppliers) to start thinking about this evolving skill set and plan for change with the appropriate knowledge and insight.

TAUS supports ideas, insights, knowledge and know-how about translators and translation in the AI economy and in the most recent and daring TAUS ebook Nunc est Tempus insights for the future of translation is explained in detail.. But, how can translators get up-to-date with the latest technologies and evolving industry expectations? Check out TAUS training courses!